journal article·2026-03

Lankford coefficient predictions using convolutional neural networks on images of sections of orientation distribution function

Lalit Kaushik

Materials Letters· Volume 407
Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Machine Learning in Materials Science · Microstructure and mechanical properties · Powder Metallurgy Techniques and Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (15)

Show source-provided references
1. Harnessing machine learning for predicting mechanical properties of lightweight Mg alloys10.1016/j.matlet.2024.137597 ↗
2. A machine learning perspective on hardness prediction in multicomponent Al-Mg based lightweight alloys10.1016/j.matlet.2024.136473 ↗
3. Building an effective deep learning model for mechanical properties prediction of steel10.1016/j.matlet.2025.139262 ↗
4. Machine learning prediction of mechanical properties in stir zone of 310 austenitic steel friction stir welds10.1016/j.matlet.2025.138100 ↗
5. Predicting hardness in refractory high-entropy alloys using machine learning10.1016/j.matlet.2025.138940 ↗
6. Discovery of a Ni-based superalloy with low thermal expansion via machine learning10.1016/j.matlet.2024.137047 ↗
7. A machine learning approach for predicting orientation-dependent elastic properties of 2D materials10.1016/j.commatsci.2025.113819 ↗
8. A machine learning-based data-driven approach for modelling anisotropic and tension-compression asymmetry behavior of elastoplastic materials using limited experiment data10.1016/j.euromechsol.2025.105733 ↗
9. Crystallographic texture prediction of torsioned aluminum wire using hybrid of machine learning and multi-scale crystal plasticity10.1016/j.matchar.2025.115000 ↗
10. Data-driven quantification of orientation dependent damage caused by voids using machine learning
11. Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation10.1016/j.jma.2023.12.007 ↗
12. Using machine learning as a supportive tool to systematically study the tensile properties of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with various microstructures: effects of texture, phase proportions and grain size10.1016/j.matchar.2025.115148 ↗
13. Nguyen · Int. J. Mech. Sci. Physics-Guided Machine Learning For Forming-Limit Assessments Of Advanced High-Strength Steels · 2025 · 287
14. A machine learning model to predict yield surfaces from crystal plasticity simulations10.1016/j.ijplas.2022.103507 ↗
15. Machine learning pipeline for Structure–Property modeling in Mg-alloys using microstructure and texture descriptors10.1016/j.actamat.2025.121132 ↗